code: def build_dataset():
      print("==> Preparing data..")
      transform_train = transforms.Compose(
          [
              transforms.RandomCrop(32, padding=4),
              transforms.RandomHorizontalFlip(),
              transforms.ToTensor(),
              transforms.Normalize(
                  (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
              ),
          ]
      )
  
      transform_test = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Normalize(
                  (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
              ),
          ]
      )
  
      trainset = torchvision.datasets.CIFAR10(
          root="./data",
          train=True,
          download=True,
          transform=transform_train,
      )
      train_loader = torch.utils.data.DataLoader(
          trainset, batch_size=128, shuffle=True, num_workers=2
      )
  
      testset = torchvision.datasets.CIFAR10(
          root="./data",
          train=False,
          download=True,
          transform=transform_test,
      )
      test_loader = torch.utils.data.DataLoader(
          testset, batch_size=100, shuffle=False, num_workers=2
      )
  
      # classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
  
      return train_loader, test_loader
  
file_path: AdaBound/demos/cifar10/main.py
imports: from __future__ import print_function
  import torch.optim
  import torch.backends.cudnn
  import torchvision
  import torchvision.transforms
  import os
  import argparse
  from models import *
  from adabound import AdaBound
label_desc: Use image node_id 2 for calling a global function with example usage: build_dataset() and returns: train_loader,  test_loader